memory-contract

You can standardize AI agent memory management by implementing a unified protocol that governs storage and retrieval procedures across sessions. This protocol uses a 5-sector model and progressive retrieval to ensure agents consistently handle episodic, semantic, procedural, emotional, and reflective memories.

Progressive retrieval in AI memory systems is a 3-layer mechanism that efficiently accesses relevant context without overwhelming the agent. It ranks memories using composite scoring based on salience, recency, and coactivation to deliver optimal relevance during context retrieval.

To prevent context overload when an AI agent recalls past decisions, use a protocol that employs progressive retrieval and composite scoring. This approach ranks memories by salience, recency, and coactivation, ensuring only the most pertinent information is retrieved.

Yes, you can use temporal knowledge graph queries within an AI agent memory protocol. The system governs memory consolidation and retrieval across multiple sectors, enabling structured temporal queries for episodic and semantic knowledge.

Yes, the AI memory protocol explicitly addresses session boot and context retrieval procedures. It retrieves active fleet plans and constraints at the start of a new session, applying defined salience and decay rates to manage memory relevance.

A unified memory protocol for AI agents requires strict adherence to defined salience and decay rates across five memory sectors. Memory consolidation processes must be properly governed to prevent retrieval degradation, and agents need full compatibility with the 3-layer retrieval system.